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Record W2782005892 · doi:10.1038/s41598-017-18679-y

Motivating Cord Blood Donation with Information and Behavioral Nudges

2018· article· en· W2782005892 on OpenAlexaff
Daniela Grieco, Nicola Lacetera, Mario Macis

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsUniversity of Toronto
FundersJohn Templeton Foundation
KeywordsCord bloodNudge theoryDonationMedicineUmbilical cordPsychological interventionSalience (neuroscience)Blood donorPsychologySocial psychologyNursingImmunology

Abstract

fetched live from OpenAlex

Umbilical cord blood is a source of hematopoietic stem cells essential to treat life-threatening diseases, such as leukemia and lymphoma. However, only a very small percentage of parents donate upon delivery. The decision to donate the cord blood occurs at a very specific time and when parents likely experience emotional, informational, and decisional overloads; these features of cord blood donation make it different from other pro-social activities. In collaboration with an OB-GYN clinic in Milan, Italy, we conducted the first randomized controlled trial that applies tools from behavioral science to foster cord blood donation, and quantified the gains that informational and behavioral "nudges" can achieve. We found that information and "soft" commitments increased donations; approaching expecting parents closer to the delivery date and providing them with multiple reminders, moreover, had the strongest impact. However, a significant portion of women who expressed consent to donate could not do so because of organizational constraints. We conclude that simple, non-invasive behavioral interventions that address information gaps and procrastination, and that increase the salience of the activity can substantially enhance altruistic donations of cord blood, especially when coupled with organizational support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.309
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2018
Admission routes1
Has abstractyes

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